Dataset Information

Optimizing future biodiversity sampling by citizen scientists.

ABSTRACT: We are currently in the midst of Earth's sixth extinction event, and measuring biodiversity trends in space and time is essential for prioritizing limited resources for conservation. At the same time, the scope of the necessary biodiversity monitoring is overwhelming funding for professional scientific monitoring. In response, scientists are increasingly using citizen science data to monitor biodiversity. But citizen science data are 'noisy', with redundancies and gaps arising from unstructured human behaviours in space and time. We ask whether the information content of these data can be maximized for the express purpose of trend estimation. We develop and execute a novel framework which assigns every citizen science sampling event a marginal value, derived from the importance of an observation to our understanding of overall population trends. We then make this framework predictive, estimating the expected marginal value of future biodiversity observations. We find that past observations are useful in forecasting where high-value observations will occur in the future. Interestingly, we find high value in both 'hotspots', which are frequently sampled locations, and 'coldspots', which are areas far from recent sampling, suggesting that an optimal sampling regime balances 'hotspot' sampling with a spread across the landscape.

SUBMITTER: Callaghan CT

PROVIDER: S-EPMC6790778 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Optimizing future biodiversity sampling by citizen scientists.

Callaghan Corey T CT Poore Alistair G B AGB Major Richard E RE Rowley Jodi J L JJL Cornwell William K WK

Proceedings. Biological sciences 20191002 1912

We are currently in the midst of Earth's sixth extinction event, and measuring biodiversity trends in space and time is essential for prioritizing limited resources for conservation. At the same time, the scope of the necessary biodiversity monitoring is overwhelming funding for professional scientific monitoring. In response, scientists are increasingly using citizen science data to monitor biodiversity. But citizen science data are 'noisy', with redundancies and gaps arising from unstructured ...[more]

PMID: 31575364

Similar Datasets

Project description:In its invasive range in Australia, the European rabbit threatens the persistence of native flora and fauna and damages agricultural production. Understanding its distribution and ecological niche is critical for developing management plans to reduce populations and avoid further biodiversity and economic losses.We developed an ensemble of species distribution models (SDMs) to determine the geographic range limits and habitat suitability of the rabbit in Australia. We examined the advantage of incorporating data collected by citizens (separately and jointly with expert data) and explored issues of spatial biases in occurrence data by implementing different approaches to generate pseudo-absences. We evaluated the skill of our model using three approaches: cross-validation, out-of-region validation, and evaluation of the covariate response curves according to expert knowledge of rabbit ecology.Combining citizen and expert occurrence data improved model skill based on cross-validation, spatially reproduced important aspects of rabbit ecology, and reduced the need to extrapolate results beyond the studied areas.Our ensemble model projects that rabbits are distributed across approximately two thirds of Australia. Annual maximum temperatures >25°C and annual minimum temperatures >10°C define, respectively, the southern and northern most range limits of its distribution. In the arid and central regions, close access to permanent water (?~ 0.4 km) and reduced clay soil composition (~20%-50%) were the major factors influencing the probability of occurrence of rabbits. Synthesis and applications. Our results show that citizen science data can play an important role in managing invasive species by providing missing information on occurrences in regions not surveyed by experts because of logistics or financial constraints. The additional sampling effort provided by citizens can improve the capacity of SDMs to capture important elements of a species ecological niche, improving the capacity of statistical models to accurately predict the geographic range of invasive species.

Project description:This paper introduces a novel method for sampling pathogens in natural environments. It uses fabric boot socks worn over walkers' shoes to allow the collection of composite samples over large areas. Wide-area sampling is better suited to studies focusing on human exposure to pathogens (e.g., recreational walking). This sampling method is implemented using a citizen science approach: groups of three walkers wearing boot socks undertook one of six routes, 40 times over 16 months in the North West (NW) and East Anglian (EA) regions of England. To validate this methodology, we report the successful implementation of this citizen science approach, the observation that Campylobacter bacteria were detected on 47% of boot socks, and the observation that multiple boot socks from individual walks produced consistent results. The findings indicate higher Campylobacter levels in the livestock-dominated NW than in EA (55.8% versus 38.6%). Seasonal differences in the presence of Campylobacter bacteria were found between the regions, with indications of winter peaks in both regions but a spring peak in the NW. The presence of Campylobacter bacteria on boot socks was negatively associated with ambient temperature (P = 0.011) and positively associated with precipitation (P < 0.001), results consistent with our understanding of Campylobacter survival and the probability of material adhering to boot socks. Campylobacter jejuni was the predominant species found; Campylobacter coli was largely restricted to the livestock-dominated NW. Source attribution analysis indicated that the potential source of C. jejuni was predominantly sheep in the NW and wild birds in EA but did not differ between peak and nonpeak periods of human incidence.IMPORTANCE There is debate in the literature on the pathways through which pathogens are transferred from the environment to humans. We report on the success of a novel method for sampling human-pathogen interactions using boot socks and citizen science techniques, which enable us to sample human-pathogen interactions that may occur through visits to natural environments. This contrasts with traditional environmental sampling, which is based on spot sampling techniques and does not sample human-pathogen interactions. Our methods are of practical value to scientists trying to understand the transmission of pathogens from the environment to people. Our findings provide insight into the risk of Campylobacter exposure from recreational visits and an understanding of seasonal differences in risk and the factors behind these patterns. We highlight the Campylobacter species predominantly encountered and the potential sources of C. jejuni.

Dataset Information

Optimizing future biodiversity sampling by citizen scientists.

Publications

Optimizing future biodiversity sampling by citizen scientists.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure